Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/127565
Authors: 
Gerard, Francois
Rokkanen, Miikka
Rothe, Christoph
Year of Publication: 
2015
Series/Report no.: 
IZA Discussion Papers 9604
Abstract: 
A key assumption in regression discontinuity analysis is that units cannot manipulate the value of their running variable in a way that guarantees or avoids assignment to the treatment. Standard identification arguments break down if this condition is violated. This paper shows that treatment effects remain partially identified in this case. We derive sharp bounds on the treatment effects, show how to estimate them, and propose ways to construct valid confidence intervals. Our results apply to both sharp and fuzzy regression discontinuity designs. We illustrate our methods by studying the effect of unemployment insurance on unemployment duration in Brazil, where we find strong evidence of manipulation at eligibility cutoffs.
Subjects: 
regression discontinuity
treatment effects
manipulation
partial identification
JEL: 
C14
C21
C26
C51
Document Type: 
Working Paper

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